Hidden labor, monitoring, and exit burdens after hospital operational AI deployment: implications for...

Hospital operational artificial intelligence (AI) can reduce visible task time, but postdeployment affordability also depends on work transferred to staff, technical teams, budgets, and vendors. This focused structured narrative Mini Review examined hidden labor, monitoring and maintenance, and exit or replacement burdens after real hospital AI deployment, with implications for resource-constrained Chinese hospitals. Reconciliation of retained candidates produced a 23-record analytic inventory, including 15 reports grouped into 12 primary deployment clusters; other records supplied adjacent, contextual or governance evidence. Exhaustive coverage is not claimed. Evidence was classified by burden domain, bearer, and three outcome levels: observed work, labor value or opportunity cost, and budget or cash expenditure. Human verification, correction, adaptation, data upkeep, and coordination persisted in several deployments, although one controlled study found no significant increase in review burden. Monitoring required continuing ownership and remediation capacity. Direct operational-AI permanent-exit evidence remained limited to one hospital lifecycle case. Chinese reports identify workflow responsibilities but do not establish AI lifecycle affordability in resource-constrained hospitals. The proposed Pilot, Scale, Modify, Share, and Stop framework comprises locally testable management propositions, not validated thresholds or Chinese cost estimates. Prospective studies should measure burdens across clinical, administrative, technical, financial, vendor, and regional actors.